| pubmed-article:21096342 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C0598941 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C0018810 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C1442792 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C1514562 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C1883221 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C0008902 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C0376249 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C1883204 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C2004457 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C2827666 | lld:lifeskim |
| pubmed-article:21096342 | lifeskim:mentions | umls-concept:C1880389 | lld:lifeskim |
| pubmed-article:21096342 | pubmed:dateCreated | 2010-11-24 | lld:pubmed |
| pubmed-article:21096342 | pubmed:abstractText | This paper examines the feasibility of accurate state classification of autonomic nervous activity (ANA) based on the power spectral pattern of the heart rate fluctuations (HRFs). Some attempts have been made to utilize artificial neural networks (ANNs) to classify HRFs for clinical diagnoses such as ischemic cardiomyopathy, arrhythmia or sleep apnea. To establish the firm bases for making such clinical diagnoses, it may be important to examine the classification accuracy for the data in physiologically well defined conditions by e.g. application of autonomic blocking agents. In this paper the three layered perceptron has been trained by the heart rate data in variety of ANS states yielded by the application of Atropine and Propranolol to 14 healthy male subjects. Six state (control, atropine and propranolol for each of the spine and upright posture) classification based on power spectrum showed average sensitivity of 67.2% and specificity 91.2%. Four state (control, atropine, propranolol and double block for either spine or upright posture) resulted in the average classification sensitivity of 75.7% and specificity 95.5%. The paper revealed that entropy bandwidth and indices originated from characteristic oscillations of blood pressure change improve the classification accuracy. | lld:pubmed |
| pubmed-article:21096342 | pubmed:language | eng | lld:pubmed |
| pubmed-article:21096342 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:21096342 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:21096342 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:21096342 | pubmed:issn | 1557-170X | lld:pubmed |
| pubmed-article:21096342 | pubmed:author | pubmed-author:OnoTakuyaT | lld:pubmed |
| pubmed-article:21096342 | pubmed:author | pubmed-author:YagiShojiS | lld:pubmed |
| pubmed-article:21096342 | pubmed:author | pubmed-author:NozawaMasakiM | lld:pubmed |
| pubmed-article:21096342 | pubmed:author | pubmed-author:YanaKazuoK | lld:pubmed |
| pubmed-article:21096342 | pubmed:author | pubmed-author:YasumotoYutak... | lld:pubmed |
| pubmed-article:21096342 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:21096342 | pubmed:volume | 2010 | lld:pubmed |
| pubmed-article:21096342 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:21096342 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:21096342 | pubmed:pagination | 1401-4 | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:meshHeading | pubmed-meshheading:21096342... | lld:pubmed |
| pubmed-article:21096342 | pubmed:year | 2010 | lld:pubmed |
| pubmed-article:21096342 | pubmed:articleTitle | State classification of heart rate variability by an artificial neural network in frequency domain. | lld:pubmed |
| pubmed-article:21096342 | pubmed:affiliation | Department of Electronic Informatics, Hosei University, Tokyo 184-8584, Japan. | lld:pubmed |
| pubmed-article:21096342 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:21096342 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
